8 citations · 28 across the 10 of their papers we have counts for
Showing 2021Show all
3 papers · 1 filter
cs.IR2021
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
Xiaohan Li, Zhiwei Liu, Stephen Guo +4
Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. H…
cs.LG2021★ 6 cited
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
Zheng Liu, Xiaohan Li, Hao Peng +2
Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich information they contain. By learning from EHRs, machine learning models can be built to help…
cs.IR2021★ 2 cited
Dynamic Graph Collaborative Filtering
Xiaohan Li, Mengqi Zhang, Shu Wu +3
Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and i…